Technology has enabled various alternative educational platforms, such as online courses. Compared to human instructors in traditional educational environments, alternative platforms often show a limited capacity to evaluate the learning progress of students and implement intervention strategies based on the evaluation. Here, we tested participants' hand motions, recorded using a computer mouse and a touchscreen, to determine if the data can predict learners' struggles. Hand motions of participants concurrently performing arithmetic and motor tasks were examined to investigate how the hand motions varied depending on the difficulty of the arithmetic task. The results indicated that working memory load affected both the temporal and spatial features of hand motions, which were predictive of participants' level of working memory load. These findings demonstrate that the assessment of struggles in learning may be achieved at relatively high accuracy with input devices commonly used by learners to access online education systems.

中文翻译：

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使用手势跟踪方法评估学习成果

技术使各种替代性教育平台成为可能，例如在线课程。与传统教育环境中的人类教练相比，替代平台通常显示出有限的能力来评估学生的学习进度，并基于评估结果实施干预策略。在这里，我们测试了参与者的手势，并使用计算机鼠标和触摸屏进行了记录，以确定数据是否可以预测学习者的挣扎。检查了同时执行算术和运动任务的参与者的手部运动，以研究手部运动如何根据算术任务的难度而变化。结果表明，工作记忆负荷会影响手部动作的时间和空间特征，可预测参与者的工作记忆负荷水平。